The task of fine-grained aircraft recognition is crucial in the field of remote sensing. Despite some progress achieved by traditional deep learning methods in addressing this challenge, they are often perceived as a “black box,” lacking transparent explanations for model decisions. Current interpretable methods based on attention mechanisms, although providing some interpretability, do not align with human thought logic. Therefore, we propose a multiscale rotation-invariant prototype network (MSRIP-Net). Our approach simulates the intuitive reasoning process of humans in identifying objects by segmenting them into multiple components. Importantly, MSRIP-Net has the capability to automatically recognize rigid components on aircraft targets without relying on additional part annotations, using only image-level class labels. In addition, our approach effectively addresses challenges presented by noise, deformations, and multiscale variations in remote sensing targets and has been comprehensively evaluated on datasets FAIR1M1.0 and Rareplane. Our results demonstrate that MSRIP-Net achieves higher accuracy compared with existing fine-grained recognition methods. Furthermore, we provide insights into the model’s decision-making process to illustrate the interpretability of our approach.

中文翻译：

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MSRIP-Net：解决飞机遥感图像细粒度识别中的可解释性和准确性挑战


细粒度的飞机识别任务在遥感领域至关重要。尽管传统深度学习方法在应对这一挑战方面取得了一些进展，但它们通常被视为“黑匣子”，缺乏对模型决策的透明解释。目前基于注意力机制的可解释方法虽然提供了一定的可解释性，但与人类的思维逻辑并不相符。因此，我们提出了一种多尺度旋转不变原型网络（MSRIP-Net）。我们的方法通过将对象分割成多个组件来模拟人类识别对象的直观推理过程。重要的是，MSRIP-Net 能够自动识别飞机目标上的刚性部件，而无需依赖额外的零件注释，仅使用图像级类别标签。此外，我们的方法有效地解决了遥感目标中的噪声、变形和多尺度变化带来的挑战，并已在数据集 FAIR1M1.0 和 Rareplane 上进行了全面评估。我们的结果表明，与现有的细粒度识别方法相比，MSRIP-Net 具有更高的准确性。此外，我们还提供了对模型决策过程的见解，以说明我们方法的可解释性。